Electronic apparatus and in particular mobile or portable electronic apparatus may be equipped with integral microphone apparatus or suitable audio inputs for receiving a microphone signal. This permits the capture and processing of suitable audio signals for processing, encoding, storing, or transmitting to further devices. For example cellular telephones may have microphone apparatus configured to generate an audio signal in a format suitable for processing and transmitting via the cellular communications network to a further device, the signal at the further device may then be decoded and passed to a suitable listening apparatus such as a headphone or loudspeaker. Similarly some multimedia devices are equipped with mono or stereo microphone apparatus for audio capture of events for later playback or transmission.
The electronic apparatus can further comprise microphone apparatus or inputs for receiving audio signals from one or more microphones and may perform some pre-encoding processing to reduce noise. For example the analogue signal may be converted to a digital format for further processing.
This pre-processing may be required when attempting to record full spectral band audio signals from a far audio signal source, the desired signals may be weak compared to background or interference noises. Some noise is external to the recorder and may be known as stationary acoustic background or environmental noise.
Typical sources of such stationary acoustic background noise are fans such as air conditioning units, projector fans, computer fans, or other machinery. Examples of machinery noise are, for example, domestic machinery such as washing machines and dishwashers, vehicle noise such as traffic noise. Further sources of interference may be from other people in the near environment, for example humming from people neighbouring the recorder at the concert, or natural noise such as wind passing through trees.
Other interference noise may be internal to the system. The Noise suppressor circuitry typically operates in the frequency domain utilizing Fast Fourier Transforms (FFT) in order to obtain sufficient frequency resolution. Since wideband signals have double the number of samples compared to narrowband signals (typically for mobile device speech applications a 8 kHz sampling frequency is defined as narrowband a 16 kHz sampling frequency is defined as wideband), the FFT length has to be doubled. This roughly doubles the needed amount of computation and memory required to process the wideband audio signals, but due to the fixed point processing the same level of H-T-accuracy cannot be provided as provided in narrowband processing.
Finite precision of audio signals also produces quantization noise. The quantization noise, when significant becomes audible and renders the listening of the signal as difficult and annoying. In speech systems this happens for example when the audio signals are processed as wideband signals (in other words having a 16 kHz sampling frequency), but only have narrowband content (in other words no significant content above 4 kHz). This situation has generally been ignored as it was assumed that it would occur infrequently, but implemented systems show that this situation may happen quite frequently. For example if a phone carrying a wideband call is attached to a Bluetooth accessory which is only narrowband capable, then only narrowband content is carried by the wideband call. Moreover, it has been observed that the quantization noise may be audible even when signals processed are true wideband signals.
Although it may be possible to use FFT with better quality to produce a partial solution it has been observed that it is impossible to solve the problem using FFT alone without using significant amount of memory and processing power and therefore having significant effect on battery power and cost for mobile devices.
The usage of two channel analysis-synthesis filterbanks that divide a wideband signal to two signals: low band and high band, has been considered as a basis of processing. However typically there is decimation of the high and low bands with aliasing compensation.
Audio signal processing of these audio signals should follow the following criteria:
1. Audio quality (the audio signal should not be distorted);
2. Memory (the filterbank should not require large amounts of memory to store the filter bank configuration in other words the filter should not need to store large numbers of values);
3. Computational complexity (the filterbank should not be sufficiently complex to require significant processor capability and thus increase the power drain on the battery for the mobile device or similar); and
4. Delay (there should not be a significantly large delay in processing as this may affect the communications pathway).
Known techniques typically produce significant amounts of quantization noise or for a suitable computation complexity and memory cannot produce sufficient quality for wideband speech purposes. Other approaches are known to require very narrow bands to be set on the filters for the low frequencies. In order to produce sufficient frequency resolution on low frequencies, many filters would be required which would be expensive in both memory and computational capacity. Further approaches produce significantly long delays and have insufficient frequency resolution for high band signals.